TY - JOUR
T1 - Multi-step carbon emissions forecasting model for industrial process based on a new strategy and machine learning methods
AU - Hu, Yusha
AU - Man, Yi
AU - Ren, Jingzheng
AU - Zhou, Jianzhao
AU - Zeng, Zhiqiang
N1 - Funding Information:
The work was supported by a grant from Fundamental Research Funds for the Central Universities (2023ZYGXZR100), a grant from the Hong Kong-Macao Joint Research Development Fund of Wuyi University (H-ZGKG, Project ID: P0043781, and a grant from Research Institute for Advanced Manufacturing (RIAM), The Hong Kong Polytechnic University (1-CD9G, Project ID: P0046135).
Publisher Copyright:
© 2024 The Institution of Chemical Engineers
PY - 2024/7
Y1 - 2024/7
N2 - The rising industrial sector capacity increases carbon emissions, necessitating a low-carbon transformation for global sustainability. Accurate multi-step forecasting of industrial carbon emissions is crucial for optimizing production, reducing energy consumption, improving efficiency, and achieving decarbonization. However, current forecasting faces challenges like model uncertainty and low accuracy with multi forecasting steps. To address these issues, this study proposed a multi-step carbon emissions forecasting model for industrial processes. Firstly, a new multi-step forecasting strategy framework was proposed. Secondly, SHapley Additive exPlanations (SHAP) and lagged autocorrelation analysis were used to extract key features affecting industrial carbon emissions. On this basis, the grey model, autoregressive moving average (ARMA) model, and least squares support vector machine (LSSVM) method were used to build forecasting models separately. The weights of the three models were solved using the induced ordered weighted average operator and the Monte Carlo search tree (MCST) method. The final forecasting results were obtained by using the weighted summation method. The data collected from the real industry were used to validate the proposed model. The results showed that the proposed model could accurately forecast industrial carbon emissions within 12 steps and its MAPE is less than 10%. The proposed multi-step industrial carbon emissions forecasting model can provide data support for industrial energy saving, carbon reduction, and green and sustainable development.
AB - The rising industrial sector capacity increases carbon emissions, necessitating a low-carbon transformation for global sustainability. Accurate multi-step forecasting of industrial carbon emissions is crucial for optimizing production, reducing energy consumption, improving efficiency, and achieving decarbonization. However, current forecasting faces challenges like model uncertainty and low accuracy with multi forecasting steps. To address these issues, this study proposed a multi-step carbon emissions forecasting model for industrial processes. Firstly, a new multi-step forecasting strategy framework was proposed. Secondly, SHapley Additive exPlanations (SHAP) and lagged autocorrelation analysis were used to extract key features affecting industrial carbon emissions. On this basis, the grey model, autoregressive moving average (ARMA) model, and least squares support vector machine (LSSVM) method were used to build forecasting models separately. The weights of the three models were solved using the induced ordered weighted average operator and the Monte Carlo search tree (MCST) method. The final forecasting results were obtained by using the weighted summation method. The data collected from the real industry were used to validate the proposed model. The results showed that the proposed model could accurately forecast industrial carbon emissions within 12 steps and its MAPE is less than 10%. The proposed multi-step industrial carbon emissions forecasting model can provide data support for industrial energy saving, carbon reduction, and green and sustainable development.
KW - Industrial carbon emissions
KW - Low-carbon production
KW - Modeling and simulation
KW - Multi-step forecasting
UR - http://www.scopus.com/inward/record.url?scp=85193480472&partnerID=8YFLogxK
U2 - 10.1016/j.psep.2024.05.043
DO - 10.1016/j.psep.2024.05.043
M3 - Journal article
AN - SCOPUS:85193480472
SN - 0957-5820
VL - 187
SP - 1213
EP - 1233
JO - Process Safety and Environmental Protection
JF - Process Safety and Environmental Protection
ER -